Media selection using a neural network

ABSTRACT

A method and system for automatically classifying a print medium entering a printing device as being a print medium type having known properties relevant to print operations. A detection system captures data indicative of optical characteristics of the incoming medium. The data is spectrally examined to derive frequency-related information. At least one neural network utilizes the frequency-related information to determine a medium type. In one embodiment, a major category network determines the medium type as one of five major medium types. Subsequently, the medium is subjected to analysis with a specific neural network for differentiating the identified major media type into narrower categories. Each neural network comprises a layer of adaptive decision-making nodes. Each node includes an activation function for processing the sum of multiple weighted inputs for generating an output. The output is directed to the output level that is at least partially utilized for a medium type determination.

TECHNICAL FIELD

[0001] The invention relates generally to printing mechanisms and moreparticularly to a system for determining the type of print media, sothat the printing mechanism can automatically select an optimal printmode for a specific type of incoming media without requiring userintervention.

BACKGROUND ART

[0002] For printers on the commercial market today, such as laser andinkjet printers, automated selection for the type of print media (e.g.,transparency media, premium media, glossy photo media, matte photomedia, etc.) is not always present. Rather than using a close-loopfeedback system for automated selection, these printers use an open-loopprocess by relying on a user to select the type of print media throughthe software driver in his/her personal computer (PC). Without correctlyselecting the proper type of print media, there is no assurance that themedia corresponds to the type selected for a particular print request.Consequently, the type of print media used for printing may not alwayscorrespond to an optimal operational mode of the printer.

[0003] Printing with an incorrectly selected media often produces poorquality images. The problem primarily stems from the fact that mostusers do not change the media type settings, even assuming that they areaware of the existing settings. Instead, the typical users print with adefault setting of the plain paper-normal mode. This is unfortunate,because if a user inserts an expensive photo media into the printer, theresulting image is sub-standard when the normal mode rather than a photomode is selected, leaving the user effectively wasting the expensivephoto media. Besides photo media, other types of media such astransparencies yield particularly poor image quality when they areprinted in the plain paper-normal mode.

[0004] One proposed system for a printer to automatically adopt anoptimal print mode for a specific type of incoming media withoutrequiring user intervention utilizes an invisible ink code. The code isprinted on each sheet of incoming media where it is read by a sensoronboard the printer. The code supplies the printer driver with practicalinformation, such as the media type, manufacturer, orientation andproperties. Armed with this information, the system is both reliable andeconomical in properly selecting the correct type of print media foroptimal performance. Thus, the user is no longer burdened by mediaselection through his/her PC. A concern with the invisible ink codesystem is that the pre-printed invisible code can become visible whenprinted over. To avoid this problem, the code is placed at the margin ofthe print medium. However, since market demand is pushing printers intobecoming high-quality photo generators, the invisible code becomes anundesirable artifact for a photographic finish requiring printing up tothe edge of the paper. Consequently, placing the invisible code at themargin creates a print defect for printing in the photo-mode.

[0005] Another system for print media type determination utilizes acombination of transmissive and reflective sensors. The transmissivesensor measures the amount of light that has passed through the printmedia and is very effective for some media type determinations, such asthe identification of a transparency. The reflective sensors receivelight reflected off the surface of the print medium at different anglesand are used to measure the specular reflectance and the diffusereflectance of the medium. By analyzing the ratio of these tworeflectance values, a specific medium type is identified. To implementthis system, a database having a look-up table of the reflective ratiosis used to correlate the ratios with various types of print media. Aconcern with this system is that new, non-characterized medium is oftenmisidentified, leading to print quality degradation. Another concern isthat several different types of media could generate the samereflectance ratio, yet have different print mode classifications.

[0006] What is needed is a method and system for reliably determiningthe type of incoming print medium, so that the printing mechanism canautomatically select a proper print mode without requiring userintervention.

SUMMARY OF THE INVENTION

[0007] The invention is a method and system that uses neural networktechniques for automatically selecting a print medium type withoutrequiring user intervention. A media detection system captures dataindicative of characteristics of an incoming medium. The data isspectrally analyzed to derive frequency-related information. At leastone media-identifying neural network utilizes the frequency-relatedinformation to determine a print medium type. A “neural network” isherein defined as an adaptive arrangement which is specifically designedto adapt on the basis of prior decisions in order to increase theaccuracy of decisions. Utilizing a feedforward architecture, themedia-identifying neural network includes a layer of decision makingnodes (i.e., the “hidden” layer). Each decision making node includes anactivation function for processing a sum of multiple weighted inputs tothe node. The output from each decision-making node may be directed to anode within the same layer for continuous processing or to a node in anoutput layer. Each node at the output layer corresponds to a major typeof print medium selection, including a transparency type, premium-papertype, plain-paper type, photo-quality type, and default type. Subsequentto identifying the print medium as one of the major medium types, aspecific neural network is utilized to narrow the identified type ofmedium into a more specific category.

[0008] The media-identifying neural network comprises an input layer ofnodes, an output layer of nodes and one “hidden” layer of nodessandwiched between the input and output layers. In a first embodiment inwhich a major network is used to identify an incoming print medium asone of the five major media print types, each node of the input layer isconfigured to receive one frequency component from the media detectionsystem. Each frequency component is derived by spectrally analyzing(e.g., performing Fourier Transform) the data captured by the mediadetection system. If there are 84 diffuse frequency components and 84specular frequency components, the input layer comprises 168 inputnodes, with each node being configured to receive one frequencycomponent and to impose a weight on the received component.

[0009] The outputs from the input nodes are directed to the “hidden” ordecision-making layer. Actual computations utilizing algorithms areperformed at the decision-making layer to determine a print medium type.The optimal number of decision-making nodes utilized in this layer isdependent on the nature of the classification. A task requiring greateraccuracy may use a greater number of decision-making nodes, while a taskrequiring greater speed may use a fewer number of nodes. In oneembodiment, the decision-making layer comprises at least sixdecision-making nodes. In a second embodiment, the layer comprises atmost ten decision-making nodes. Each decision-making node may beconfigured to receive 168 weighted inputs and emit one output. Anactivation function is applied to the sum of the weighted inputs,together with a bias weight for each decision-making node to produce oneoutput.

[0010] The decision-making nodes are configured to generate a decisionfor designating a print medium type for the incoming medium. Each of thenodes in the output layer corresponds to one of the major media types.While the process may designate the subject print medium as one of atransparency type, premium-paper type, plain-paper type, photo-qualitytype and default type, other types of categorization can be selectedwithout diverging from the scope of the invention.

[0011] In the first embodiment, the print medium is further subjected toanalysis within a specific neural network to differentiate the selectedmajor media type into narrower categorizes. For example, after adetermination by a major network that an incoming print medium is a“photo-quality type,” a specific neural network is utilized to furtherdifferentiate the “photo-quality type” as one of a: (1) default type,(2) Gossimer type, (3) combined type, and (4) very glossy type.

[0012] In a second embodiment, the 168 frequency components are analyzedto determine a print media type of the incoming print medium utilizingother categorizing means, without being subjected to analysis within amajor neural network. Specifically, after identifying the print mediumas one of the major media types utilizing other categorizing techniques,the incoming medium is subjected to the specific neural network to moreclearly differentiate the medium as being one that fits within anarrower category.

[0013] The media-identifying network architecture is dependent on thetypes of training algorithms used for defining the network. Duringtraining in the “supervised” mode, a training set of print media for aparticular class (e.g., a transparency type) is provided to the printingmechanism. The decision-making nodes are set to be “ON” for thatparticular class and “OFF” for the other classes. Each node isassociated with a bias term, i.e., a weight, to be applied to each inputvalue. A weight determines how much relative effect an input value hason an output value for a given node. Initially, the values for theweights are selected at random. As training continues, error reductionalgorithms adjust the actual outputs to the target outputs by reducingthe error space for each of the connections in the network. Theadjustment utilizes a genetic algorithm or a simulated annealingalgorithm to determine a global minima for each connection. Anassociated weight corresponding to the global minima reduces the measureof error in the network's results. Finally, a conjugate descent isperformed to determine the direction of the global minima. The trainingprocess continues until the error value is within an acceptable targetrange.

[0014] In one aspect of the invention, an incoming print medium thatdoes not correspond to one of a desired type (i.e., transparency type,premium-paper type, plain-paper type and photo-quality type) is directedto an output node designated as the default type. A faulty training setof print media that does not correspond to one of the desired types maybe input to the printing mechanism to teach the system to recognize anon-desired type of incoming print medium.

[0015] One of the advantages of the invention is that by utilizing amedia-identifying neural network, the system is flexible and can easilybe updated to detect other types of print media.

BRIEF DESCRIPTION OF THE DRAWINGS

[0016]FIG. 1 is a process flow diagram for classifying an incoming printmedium into pre-determined categories entering a printing device inaccordance with the invention.

[0017]FIG. 2 is a print media detection system of the printing device ofFIG. 1 for capturing data reflected off the incoming print medium.

[0018]FIG. 3 is a major media-identifying neural network in accordancewith the invention for categorizing an incoming print medium as one ofthe five major media types.

[0019]FIG. 4 is a media type table listing exemplary specific mediatypes for each of the five major media types.

[0020]FIG. 5 is a process flow of steps for training the neural networkof FIG. 3.

DETAILED DESCRIPTION

[0021] In accordance with the invention, FIG. 1 is a process flow ofsteps for classifying an incoming print medium entering a printingdevice into pre-determined categories without requiring userintervention. In step 10, data indicative of characteristics of theincoming medium is collected. A transformation step 12 is performed toplace the data collected in step 10 into a suitable format forsubsequent analysis. Following the transformation step 12, a majorcategory determination step 14 and a subsequent specific categorydetermination step 16 are performed utilizing at least onemedia-identifying neural network. In one embodiment, the incoming printmedium is categorized by an adaptive major neural network as one of thefollowing major media types in step 14: (1) transparency type, (2)premium-paper type, (3) plain-paper type, (4) photo-quality type and (5)default type. Subsequent to identifying a major media type, the incomingprint medium is subjected to an adaptive specific neural network foridentifying a specific media type in step 16. Subsequently, anoperational print mode is selected in step 18. In response to theselection in step 18, the printing device is configured to utilize aparticular set of print parameters. The printing device may be any typeof device utilized for printing, such as inkjet printers and laserprinters.

[0022] With reference to step 10 of FIG. 1, FIG. 2 shows a print mediadetection system 20 of a printer 21 comprising: (1) an illuminatingsource 22 configured to direct a modified light beam 24 onto an incomingprint medium 26 at a region of interest 28, (2) a diffuse sensor 30configured to receive a diffuse reflectance light beam 32 reflected offfrom the region of interest and (3) a specular sensor 34 configured toreceive a specular reflectance light beam 36 reflected off from theregion of interest. For capturing print medium data, the illuminatingsource may be an LED (light emitting diode) for emitting a single pulseof light for each sampling. The emitted pulse may be diffracted by anoptical element (not shown) into the modified beam that is focused ontothe region of interest. After striking the region of interest, themodified beam is reflected off the medium as both the diffusereflectance beam and the specular reflectance beam. The diffusedeflected beam has a flame-light scattering of rays arranged in aLambertian distribution. The specular deflected beam is reflected offthe region of interest at the same angle at which the modified beamimpinges the region of interest. The diffuse sensor 30 and the specularsensor 34 convert the detected beams into signals for subsequentprocessing. A controller 38 that is operationally coupled to theilluminating source, diffuse sensor and specular sensor by respectivechannels 40 controls the illumination of the light source and thecapturing of the data reflected off from the illuminated region ofinterest.

[0023] With reference to step 12 of FIG. 1, the signals corresponding tothe detected diffuse reflectance beam 32 and the specular reflectancebeam 36 are subjected to data transformation into a suitable format forsubsequent analysis. Prior to the transformation, the signals may besubjected to a Hanning or Welch windowing function, but this is notcritical to the invention. Following the windowing function, a discreteFourier Transform function is performed on the data to provide 84frequency-related components for the diffuse reflectance signals and 84frequency-related components for the specular reflectance signals. Asubsequent pre-scaling step, such as subjecting each of the 168frequency component to a log(n) or sqrt(n) function, may be performed.

[0024] The selection of a major media type under the major categorydetermination step 14 of FIG. 1 can be performed by either of twodifferent embodiments. In a first embodiment with reference to FIG. 3,the 84 diffuse frequency components and the 84 specular frequencycomponents are analyzed within a major media-identifying neural network42 for categorizing the incoming print medium as one of the five majormedia print types. The major neural network 42 is configured to processthe data in a feedforward direction. It comprises an input layer ofnodes 44, a “hidden” or decision-making layer of nodes 46 and an outputlayer of nodes 48. The neural network processes in the feedforwarddirection when the nodes in one layer send their outputs to the nodes ina next layer (e.g., decision-making layer) without receiving any inputback from the nodes in the next layer. This is shown by the direction ofsignals flowing in a “forward” direction from layer 44 to layer 46 andfinally to layer 48. Since there are a total of 168 frequency-relatedcomponents (84 for the diffuse reflectance data and 84 for the specularreflectance data), there are a total of 168 corresponding nodes in theinput layer, with each node configured to receive each of the 168frequency components. No processing is performed by any node in theinput layer. Rather, the input nodes are a semantic construct utilizedto represent the input layer.

[0025] Within the decision-making layer 46, there are sixdecision-making nodes. Each decision-making node may be configured toreceive weighted values from the nodes in the preceding layer (i.e., theinput layer 44) and from the nodes within the same layer (i.e.,decision-making layer 46). Each decision-making node has a connectiveweight associated with each input, multiplies each input value by itsassociated weight, and sums these values for all of the inputs. The sumis then used as input to an activation function to produce an output forthat node. An associated bias term for each function may be utilized foradjusting the output. The activation function is typically a sigmoidfunction, such as a logistic function or a hyperbolic tangent function.The output from the selected activation function may be directed to anode within the same layer (i.e., decision-making layer) for furtherprocessing or to a node in the next layer (i.e., output layer).

[0026] While the invention is shown as comprising six decision-makingnodes within the decision-making layer, there can be a greater or lessernumber of nodes. In an alternative embodiment, the number ofdecision-making nodes is ten. The optimal number of nodes is dependenton various factors, such as the types of training algorithms utilizedand the desired accuracy for the classification scheme. Moreover, therecan be a greater number of decision-making layers 46 within the network.Again, the optimal number of layers may be dependent on the types oftraining algorithms and the desired accuracy of the classificationsystem.

[0027] In the preferred embodiment, there are five nodes at the outputlayer 48. Each output node corresponds to a particular print mediumtype. An incoming print medium subjected to analysis with the neuralnetwork is categorized as one of the five print media types. Theyinclude: (1) a transparency type, (2) a premium-paper type, (3) aplain-paper type, (4) a photo-quality type and (5) a default type. Whilethe invention is described as having five major media print types, therecan be a fewer number or a greater number of major media print types.Moreover, there can be other types of print media selected forcategorization, such as a bonded-paper type, without diverging from thescope of the invention.

[0028] Referring to the specific category determination step 16 of FIG.1, the print medium is further subjected to analysis within a specificmedia-identifying neural network after being categorized as one of thefive major media types by the major neural network 42. Analysis withinthe specific neural network differentiates a major media type selectioninto narrower categories. As an example, after determining that theincoming print medium is a “transparency type,” a specific neuralnetwork is utilized to further differentiate the “transparency type” aseither a “default type” or a “HP type.” FIG. 4 shows a media type table52 listing exemplary specific media types for each of the five majormedia types on row 54.

[0029] The architecture of the specific neural network is similar to thearchitecture of the major neural network 42 of FIG. 3. Specifically, thespecific neural network comprises an input layer, at least onedecision-making layer and an output layer. The number of nodes used ineach layer as well as the number of layers and the connective weightsassociated with each node in the decision-making layer of the specificneural network are dependent on the same factors identified whenreferring to the major neural network.

[0030] Referring to FIG. 2, the major neural network 42 is configured toreceive frequency data from the controller 38 for a major media typedetermination. After identifying the incoming medium 26 as one of thefive major media types, the medium is further subjected to analysiswithin the specific media-identifying neural network 43 for a specificmedia type determination. Subsequently, a print mode is selected by aprinter driver 45 for the incoming medium.

[0031] In a second embodiment under the major category determinationstep 14 of FIG. 1, the 168 frequency components are categorized as oneof the five major media types without being subjected to the majormedia-identifying neural network 42 of FIG. 3. Rather, othercategorizing techniques that do not include a neural network areutilized for the media type selection in step 14. In an exemplaryembodiment, the ratio of the spectral signals corresponding to thediffuse reflectance light beam 32 (FIG. 2) and the specular reflectancelight beam 36 are analyzed to determine a major print medium type.Following a determination of the incoming print medium as being one ofthe five major media types, the print medium is subjected to analysiswithin the specific media-identifying neural network 43 (FIG. 2) in thespecific category determination step 16 of FIG. 1.

[0032] As was previously stated, each decision-making node is associatedwith a connective weight. For a given decision-making node, theassociated weight corresponding to an input determines the relativestrength an input value has on the output value. Consequently, theweights determine the classification for a given set of input data. Theweights assigned to each input are determined during the training phase.

[0033]FIG. 5 shows a process flow of steps for training the neuralnetwork 42 of FIG. 3. In step 56, the weights are initialized to randomvalues or to preselected values. In step 58, a set of training data fora particular class (e.g., transparency type) is provided to the inputnodes of the network for training. In supervised training, many samplespertaining to a specific class are input to the network to “teach” thesystem and recognize characteristics indicative of the selected class. Amedia detection system similar to the detection system 20 of FIG. 2captures the diffuse and specular reflectance data reflected off atraining medium. A discrete Fourier Transform function is performed onthe data to produce 84 frequency-related components for the diffusereflectance signals and 84 frequency-related components for the specularreflectance signals. Analysis by the decision-making nodes for thatparticular set of training data input to the network in step 58 resultsin the network outputting a value corresponding to that particularclass.

[0034] In step 60, error reduction algorithms adjust the actual outputsto the target outputs by reducing the error space for each of theconnective weights in the network. The adjustment utilizes geneticalgorithms or simulated annealing algorithms to determine a globalminima for each connection. An associated weight corresponding to aglobal minima reduces the measure of error in the network's results.Finally, a conjugate descent is performed to determine the direction ofthe global minima. While the invention is described as utilizing acombination of genetic or simulating annealing algorithms in conjunctionwith performing a conjugate descent, other error reduction means, suchas back propagation means without utilizing the identified algorithms,may be used to approximate the actual associated weights to the targetvalues.

[0035] In step 62, test samples are applied to the network to validatethe accuracy of the system. If the error space is greater than thepredetermined threshold value, the training process continues until theerror space is found to be less than the pre-determined value. Thisprocess is repeated with the training data until the number of mistakenclassifications is lower than the pre-determined threshold value. Aseparate training set may be used for each of the major media types,requiring steps of FIG. 5 to be repeated.

[0036] Moreover, faulty training sets of print media havingcharacteristics not indicative of a transparency type, premium-papertype, plain-paper type, or photo-quality type are provided to thenetwork to train the system to classify a corresponding incoming printmedium as a “default type.” Finally, while FIG. 5 is described astraining the major neural network 42 for categorization, the samesequence of steps can be used for training the specific neural networkfor differentiating an identified major media type into narrowercategories.

What is claimed is:
 1. A method for classifying incoming media enteringa printing device comprising the steps of: optically viewing a portionof an incoming medium to generate data indicative of characteristics ofsaid incoming medium; subjecting said data to at least one neuralnetwork for determining a medium type, said neural network beingadaptive with respect to determinations of assignments of weights forapplication to said data, said assignments being based upon adaptivetraining of said neural network; and selecting an operational print modefor said printing device at least partially based on an output of saidneural network.
 2. The method of claim 1 further comprising a step oftraining said at least one neural network, said step of trainingincluding providing said neural network with a training set of printmedia for each of a plurality of preselected medium types, said trainingsets having attributes indicative of characteristics of said mediumtypes.
 3. The method of claim 2 wherein said step of training furtherincludes utilizing error reduction algorithms for adjusting actualneural network outputs to target outputs by reducing an error spaceconnecting nodes in said neural network.
 4. The method of claim 3wherein said step of utilizing said error reduction algorithms includesidentifying global minima by employing one of a genetic algorithm and asimulated annealing algorithm.
 5. The method of claim 3 furthercomprising a step of performing a conjugate descent to determine adirection of said global minima.
 6. The method of claim 2 wherein saidstep of training includes providing said neural network with faultytraining sets of media resulting in classifying said incoming medium asa default type category.
 7. The method of claim 1 wherein said step ofoptically viewing includes capturing diffuse reflectance data andspecular reflectance data from said portion of said incoming medium. 8.The method of claim 7 further comprising a step of determining spatialfrequencies for said data, including performing Fourier Transform toprovide a plurality of frequency-related components for said diffusereflectance data and a plurality of frequency-related components forsaid specular reflectance data.
 9. The method of claim 1 wherein saidstep of optically viewing said incoming medium includes providing ahard-copy print medium for analysis.
 10. A method of making an automatedmedia selection for incoming print media comprising the steps of:establishing an evaluation system for decision making having multiplelayers, including using automated processing techniques to define aplurality of nodes arranged in an input layer, an adaptive layer, and anoutput layer, said nodes in said adaptive layer being connected with aplurality of weighted inputs, said weighted inputs being adaptivelydetermined by using algorithms for approximating inputs of said nodes topre-determined values from a plurality of training media; registeringdata relevant to an incoming print medium; processing said data throughsaid evaluation system for selectively classifying said incoming printmedium to a type of print medium; and selecting an operational printmode for said incoming print medium as a response to a determinationduring said processing of said data through said evaluation system. 11.The method of claim 10 wherein said step of selecting includesselectively classifying said print medium as one of a transparency type,premium-paper type, plain-paper type, photo-quality type and defaulttype.
 12. The method of claim 10 wherein said step of establishingincludes providing a feedforward evaluation system for neural networkprocessing.
 13. The method of claim 10 further comprising a step ofspectrally analyzing said data relevant to said incoming print medium toprovide frequency-related values.
 14. The method of claim 13 whereinsaid step of analyzing further includes processing saidfrequency-related values within a major neural network for identifying aprint medium type for said incoming print medium.
 15. The method ofclaim 14 wherein said step of identifying said print medium type furtherincludes subjecting said data relevant to said incoming print medium toa specific neural network for differentiating said print medium into amore specific category within said print medium type.
 16. A classifyingsystem for categorizing incoming print media comprising: a mediadetection system for capturing data associated with an incoming printmedium; and a media-identifying neural network having an input stage, anoutput stage and at least one decision-making stage, saiddecision-making stage comprising a plurality of classification nodes,each of said classification nodes configured to receive a plurality ofweighted inputs from other classification nodes within saiddecision-making stage and from said input stage for generating anoutput, said output being representative of a type of print medium forsaid incoming print medium.
 17. The classifying system of claim 16wherein said media detection system comprises: a light source configuredto provide an illumination onto a region of interest of said incomingprint medium; a diffuse sensor configured to receive diffuse reflectancefrom said region of interest; and a specular sensor configured toreceive specular reflectance from said region of interest.
 18. Theclassifying system of claim 16 wherein said media-identifying neuralnetwork comprises a plurality of first nodes at said input stage, eachof said first nodes being configured to receive a frequency componentvalue corresponding to said data captured by said media detectionsystem.
 19. The classifying system of claim 16 wherein saidmedia-identifying neural network comprises five nodes at said outputstage, each said output stage being specific to a media type.
 20. Theclassifying system of claim 16 further comprising a second neuralnetwork that is connected to receive said outputs from said output stageof said media-identifying neural network to subcategorize said incomingprint medium as being one having specified properties.